Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/74951
Title: Learning music emotions via quantum convolutional neural network
Authors: Chen, G 
Liu, Y 
Cao, J 
Zhong, S
Liu, Y
Hou, Y
Zhang, P
Keywords: Convolutional neural network
Music emotion
Quantum mechanics
Superposition collapse
Issue Date: 2017
Publisher: Springer Verlag
Source: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics), 2017, v. 10654 LNAI, p. 49-58 How to cite?
Journal: Lecture notes in computer science (including subseries Lecture notes in artificial intelligence and lecture notes in bioinformatics) 
Abstract: Music can convey and evoke powerful emotions. But it is very challenging to recognize the music emotions accurately by computational models. The difficulty of the problem can exponentially increase when the music segments delivery multiple and complex emotions. This paper proposes a novel quantum convolutional neural network (QCNN) to learn music emotions. Inheriting the distinguished abstraction ability from deep learning, QCNN automatically extracts the music features that benefit emotion classification. The main contribution of this paper is that we utilize measurement postulate to simulate the human emotion awareness in music appreciation. Statistical experiments on the standard dataset shows that QCNN outperforms the classical algorithms as well as the state-of-the-art in the task of music emotion classification. Moreover, we provide demonstration experiment to explain the good performance of the proposed technique from the perspective of physics and psychology.
Description: International Conference on Brain Informatics, BI 2017, Beijing, China, 16-18 November, 2017
URI: http://hdl.handle.net/10397/74951
ISBN: 9783319707716
ISSN: 0302-9743
EISSN: 1611-3349
DOI: 10.1007/978-3-319-70772-3_5
Appears in Collections:Conference Paper

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